3 ms·

Validating the wx is extremely hard. For instance, if you make a forecast, what do you use as the 'truth'? A weather station? Trust me, I've worked with plenty

Validating the wx is extremely hard. For instance, if you make a forecast, what do you use as the 'truth'? A weather station? Trust me, I've worked with plenty of station data, and it is trash. So now you are comparing a weather forecast against some suspect trash validation set. Nobody knows! The fact is, predictions will keep getting gradually better, more high res. But the fact our atmosphere & earth system is insanely complex. The system is chaotic so tiny small changes in each model run produce different results... We have code to represent some basic physics but still dont understand many of the important things. We still dont know exactly the rain process & snow formation process. We are trying to pinpoint the exact moment when dust & water vapor magically form into a cloud droplet so we can better describe the rain process. THEN we have to translate all of this insane math and physics into code.

labster 8y ago
Hi fellow meterologist hacker. I too have a B.S. in atm sci and enough years in grad school that I'm still paying for it lol. Yeah, the extent to which models are verified against their own data is pretty interesting -- even the reanalysis data only represents a best guess of a place with physics similar to Earth. It includes a lot of advected observations, thousands of miles from the original observation site, but still useful because at least you know something about the air over the oceans. Even if we really do get a good handle on cloud physics, ultimately there are limits on how much we can model. Parameterizations have to happen, because of limits to computer time -- a 24h forecast completed in 26h is worthless. I'm honestly surprised that they've been pushing to get models to third order and higher approximations, because the errors in your initial conditions are larger, but apparently every little bit helps. But forecasts are pretty good considering that weather modeling is a initial value problem where we only know the initial conditions at a few observation points of varying quality, a boundary value problem where the boundaries can have complex interactions with the internal state, and simulation run time really matters.

labster 8y ago

Hi fellow meterologist hacker. I too have a B.S. in atm sci and enough years in grad school that I'm still paying for it lol. Yeah, the extent to which models are verified against their own data is pretty interesting -- even the reanalysis data only represents a best guess of a place with physics similar to Earth. It includes a lot of advected observations, thousands of miles from the original observation site, but still useful because at least you know something about the air over the oceans. Even if we really do get a good handle on cloud physics, ultimately there are limits on how much we can model. Parameterizations have to happen, because of limits to computer time -- a 24h forecast completed in 26h is worthless. I'm honestly surprised that they've been pushing to get models to third order and higher approximations, because the errors in your initial conditions are larger, but apparently every little bit helps. But forecasts are pretty good considering that weather modeling is a initial value problem where we only know the initial conditions at a few observation points of varying quality, a boundary value problem where the boundaries can have complex interactions with the internal state, and simulation run time really matters.